Intelligent traffic signal control method

Abstract

The invention discloses an intelligent traffic signal control method. The method comprises the steps of obtaining road network traffic flow operation data and evaluating a current traffic jam value; constructing and training a traffic signal control model; and using the trained traffic signal control model to determine the road network state and obtain an optimal traffic signal control strategy capable of reducing road network traffic congestion. According to the method, traffic road network state data is processed through the convolutional neural network, the space-time dependence characteristics of traffic flow are effectively extracted, and meanwhile, the problem of huge signal phase combination space is solved in a value function iterative approximation mode by adopting an Actor-Critic reinforcement learning method, so that an intelligent traffic signal control model is established. The model is high in portability and can be suitable for various types of road traffic conditions.

Description

technical field [0001] The invention belongs to the technical field of traffic signal control, and in particular relates to an intelligent traffic signal control method. Background technique [0002] In the 1960s, many scholars proposed different methods to control the traffic signal phase. The fixed-time control method, which uses historical data to determine the optimal traffic signal period and phase, is one of the earliest traffic signal control methods. In 1958, Webster proposed a method to find the best signal cycle and green signal ratio by comparing the flow rate of each lane at the intersection to determine the key traffic flow direction. However, the fixed timing method is a phase control scheme based on historical traffic flow data, so it does not have the function of predicting traffic demand, so it is difficult to adapt to changes in real-time traffic conditions. However, because of its simple calculation and convenient operation, it is still widely used in ma...

AI Technical Summary

Problems solved by technology

However, the establishment of an adaptive control system requires the calibration of many environmental parameters, and the system is highly targeted in the region, making it difficult to migrate the system

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